Research Information System
International Civil Engineering and Architecture Conference (ICEARC’19)-2019, Trabzon, Turkey, 17 April 2019
In this study, it is aimed to estimate the suspended sediment concentration (SSC) from the stream flow rate (Q) and turbidity (T) measurements. The SSC, Q, and T were monitored seasonally in situ during a period of three years from 2015 to 2017 in the Aksu, Söğütlü, Karadere, Solaklı, İyidere, and Fırtına streams in the Eastern Black Sea Basin, Turkey. The conventional regression analysis (CRA) and artificial neural networks (ANNs) were used for modeling. The CRA was performed using three different equations. In addition to different learning rate and momentum, two different modeling algorithms, namely Levenberg-Marquardt (LM) and gradient descent with momentum, were used in the ANNs. In all models, four stations were used in the model training, one station was used in the validation phase and the remaining station was used to test the model. The estimation models were compared with root mean square error, mean absolute error, and determination coefficient. It has been seen that the ANNs are the best result in applied regression analysis and the ANNs models. The best model has succeeded in realistically reflecting the change of the peak values. It was concluded that it has applicable results on different streams.